Immunosuppression Practices in Liver Transplantation: A Survey of North American Centers.
Bibliographic record
Abstract
OBJECTIVES: There is a clear lack of clinical evidence guiding immunosuppressive management in long-term stable liver transplant recipients. As a result, anecdotal experience suggests wide variability across transplant centers. We aimed to identify patterns of immunosuppression practices in liver transplant centers across Canada and the United States. MATERIALS AND METHODS: From February 9 to May 31, 2015, we invited clinicians from all liver transplant centers in Canada and the United States to answer a 6-question survey generated using SurveyMonkey. RESULTS: Seventeen respondents from 15 liver transplant centers completed the survey. Although immun-suppressive practices are relatively uniform for induction and early maintenance therapy, significant variations exist in the management of long-term immunosuppression in stable transplant recipients with a relative lack of minimization protocols. CONCLUSIONS: Our survey confirms a wide variability in immunosuppression practices across Canadian and US liver transplant centers. Research and practice priorities include design of pragmatic randomized controlled trials and development of clinical practice guidelines to standardize immunosuppressive management of long-term stable liver transplant recipients with a focus on immunosuppression minimization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".